# Plot a comparison results for fitted or validated subgroup identification models Plots comparison of results for estimated subgroup treatment effects ```r plotCompare( ..., type = c("boxplot", "density", "interaction", "conditional"), avg.line = TRUE ) ``` ## Arguments - `...`: the fitted (model or validation) objects to be plotted. Must be either objects returned from `fit.subgroup()` or `validate.subgroup()` - `type`: type of plot. `"density"` results in a density plot for the results across all observations (if `x` is from `fit.subgroup()`) or if `x` is from `validate.subgroup()` across iterations of either the bootstrap or training/test re-fitting. For the latter case the test results will be plotted. `"boxplot"` results in boxplots across all observations/iterations of either the bootstrap or training/test re-fitting. For the latter case the test results will be plotted. `"interaction"` creates an interaction plot for the different subgroups (crossing lines here means a meaningful subgroup). `"conditional"` plots smoothed (via a GAM smoother) means of the outcomes as a function of the estimated benefit score separately for the treated and untreated groups. - `avg.line`: boolean value of whether or not to plot a line for the average value in addition to the density (only valid for `type = "density"`) ## Examples ```r library(personalized) set.seed(123) n.obs <- 100 n.vars <- 15 x <- matrix(rnorm(n.obs * n.vars, sd = 3), n.obs, n.vars) # simulate non-randomized treatment xbetat <- 0.5 + 0.5 * x[,1] - 0.5 * x[,4] trt.prob <- exp(xbetat) / (1 + exp(xbetat)) trt01 <- rbinom(n.obs, 1, prob = trt.prob) trt <- 2 * trt01 - 1 # simulate response delta <- 2 * (0.5 + x[,2] - x[,3] - x[,11] + x[,1] * x[,12]) xbeta <- x[,1] + x[,11] - 2 * x[,12]^2 + x[,13] xbeta <- xbeta + delta * trt # continuous outcomes y <- drop(xbeta) + rnorm(n.obs, sd = 2) # create function for fitting propensity score model prop.func <- function(x, trt) { # fit propensity score model propens.model <- cv.glmnet(y = trt, x = x, family = "binomial") pi.x <- predict(propens.model, s = "lambda.min", newx = x, type = "response")[,1] pi.x } subgrp.model <- fit.subgroup(x = x, y = y, trt = trt01, propensity.func = prop.func, loss = "sq_loss_lasso", # option for cv.glmnet, # better to use 'nfolds=10' nfolds = 3) # option for cv.glmnet subgrp.model.o <- fit.subgroup(x = x, y = y, trt = trt01, propensity.func = prop.func, # option for cv.glmnet, # better to use 'nfolds=10' loss = "owl_logistic_flip_loss_lasso", nfolds = 3) plotCompare(subgrp.model, subgrp.model.o) ``` ## See Also `fit.subgroup` for function which fits subgroup identification models and `validate.subgroup` for function which creates validation results.

plotCompare function